- Tidyverse Will Not Download Into R Studio Mac Osx
- Tidyverse Will Not Download Into R Studio Mac Free
- Tidyverse Will Not Download Into R Studio Mac Os
- Tidyverse Will Not Download Into R Studio Mac Download
- Importing data into R is a necessary step that, at times, can become time intensive. To ease this task, RStudio includes new features to import data from: csv, xls, xlsx, sav, dta, por, sas and stata files.
- When nothing else worked, I updated my RStudio (which did not help either). However, after that, I update my R version and that fixed the issue for me. Swaha294 July 23, 2020, 3:31am #5.
I tried to install two packages in R Studio: tidyverse and quantmod. However both give me errors and I can't understand why (googling doesn't help to understand the problem). For tidy verse I get. The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures. Install the complete tidyverse with: install.packages('tidyverse').
This chapter includes the following recipes:
What you should know before you begin
Data tidying refers to reshaping your data into a tidy data frame or tibble. Civilization 5 free download full game mac. Data tidying is an important first step for your analysis because every tidyverse function will expect your data to be stored as Tidy Data.
Tidy data is tabular data organized so that:
- Each column contains a single variable
- Each row contains a single observation
Tidy data is not an arbitrary requirement of the tidyverse; it is the ideal data format for doing data science with R. Tidy data makes it easy to extract every value of a variable to build a plot or to compute a summary statistic. Tidy data also makes it easy to compute new variables; when your data is tidy, you can rely on R’s rowwise operations to maintain the integrity of your observations. Moreover, R can directly manipulate tidy data with R’s fast, built-in vectorised observations, which lets your code run as fast as possible.
The definition of Tidy Data isn’t complete until you define variable and observation, so let’s borrow two definitions from R for Data Science:
- A variable is a quantity, quality, or property that you can measure.
- An observation is a set of measurements made under similar conditions (you usually make all of the measurements in an observation at the same time and on the same object).
3.1 Create a tibble manually
You want to create a tibble from scratch by typing in the contents of the tibble.
tribble() creates a tibble and tricks you into typing out a preview of the result. To use
tribble(), list each column name preceded by a
~, then list the values of the tribble in a rowwise fashion. If you take care to align your columns, the transposed syntax of
tribble() becomes a preview of the table.
You can also create a tibble with
tibble(), whose syntax mirrors
Tidyverse Will Not Download Into R Studio Mac Osx
3.2 Convert a data frame to a tibble
You want to convert a data frame to a tibble.
3.3 Convert a tibble to a data frame
You want to convert a tibble to a data frame.
as_data_frame(), which is an alias for
Tidyverse Will Not Download Into R Studio Mac Free
3.4 Preview the contents of a tibble
You want to get an idea of what variables and values are stored in a tibble.
When you call a tibble directly, R will display enough information to give you a quick sense of the contents of the tibble. This includes:
- the dimensions of the tibble
- the column names and types
- as many cells of the tibble as will fit comfortably in your console window
3.5 Inspect every cell of a tibble
You want to see every value that is stored in a tibble.
View() (with a capital V) opens the tibble in R’s data viewer, which will let you scroll to every cell in the tibble.
3.6 Spread a pair of columns into a field of cells
You want to pivot, convert long data to wide, or move variable names out of the cells and into the column names. These are different ways of describing the same action.
type, which is a column that repeats the variable names
population. To make
table2 tidy, you must move
population values into their own columns.
spread(), assign the column that contains variable names to
key. Assign the column that contains the values that are associated with those names to
- Make a copy of the original table
- Remove the
valuecolumns from the copy
- Remove every duplicate row in the data set that remains
- Insert a new column for each unique variable name in the
Fill the new columns with the values of the
valuecolumn in a way that preserves every relationship between values in the original data setSince this is easier to see than explain, you may want to study the diagram and result above.
Each new column created by
spread() will inherit the data type of the
value column. If you would to convert each new column to the most sensible data type given its final contents, add the argument
convert = TRUE.
3.7 Gather a field of cells into a pair of columns
You want to convert wide data to long, reshape a two-by-two table, or move variable values out of the column names and into the cells. These are different ways of describing the same action.
table4a is a two-by-two table with the column names
2000. These names are values of a
year variable. The field of cells in
table4a contains counts of TB cases, which is another variable. To make
table4a tidy, you need to move year and case values into their own columns.
gather() is the inverse of
gather() collapses a field of cells that spans several columns into two new columns:
- A column of former “keys”, which contains the column names of the former field
- A column of former “values”, which contains the cell values of the former field
gather(), pick names for the new
value columns, and supply them as strings. Then identify the columns to gather into the new key and value columns.
- Create a copy of the original table
- Remove the identified columns from the copy
- Add a key column with the supplied name
- Fill the key column with the column names of the removed columns, repeating rows as necessary so that each combination of row and removed column name appears once
- Add a value column with the supplied name
Fill the value column with the values of the removed columns in a way that preserves every relationship between values and column names in the original data setSince this is easier to see than explain, you may want to study the diagram and result above.
Identify columns to gather
You can identify the columns to gather (i.e. remove) by:
- index (numbers)
- inverse index (negative numbers that specifiy the columns to retain, all other columns will be removed.)
select()helpers that come in the dplyr package
So for example, the following commands will do the same thing as the solution above:
By default, the new key column will contain character strings. If you would like to convert the new key column to the most sensible data type given its final contents, add the argument
convert = TRUE.
3.8 Separate a column into new columns
You want to split a single column into multiple columns by separating each cell in the column into a row of cells. Each new cell should contain a separate portion of the value in the original cell.
population values in a single column named
rate. To tidy
table3, you need to separate
rate into two columns: one for the
cases variable and one for the
col the name of the column to split, and pass
into a vector of names for the new columns to split
col into. You should supply one name for each new column that you expect to appear in the result; a mismatch will imply that something went wrong.
- Create a copy of the original data set
- Add a new column for each value of
into. The values will become the names of the new columns.
- Split each cell of
colinto multiple values, based on the locations of a separator character.
- Place the new values into the new columns in order, one value per column
colcolumn. Add the argument
remove = FALSEto retain the
colcolumn in the final result.Since this is easier to see than explain, you may want to study the diagram and result above.
Each new column created by
separate() will inherit the data type of the
col column. If you would like to convert each new column to the most sensible data type given its final contents, add the argument
convert = TRUE.
Control where cells are separated
separate() will use non-alpha-numeric characters as a separators. Pass a regular expression to the
sep argument to specify a different set of separators. Alternatively, pass an integer vector to the
sep argument to split cells into sequences that each have a specific number of characters:
sep = 1will split each cell between the first and second character.
sep = c(1, 3)will split each cell between the first and second character and then again between the third and fourth character.
sep = -1will split each cell between the last and second to last character.
Separate into multiple rows
separate_rows() behaves like
separate() except that it places each new value into a new row (instead of into a new column).
separate_rows(), follow the same syntax as
3.9 Unite multiple columns into a single column
You want to combine several columns into a single column by uniting their values across rows.
table5 splits the
year variable across two columns:
year. To make
table5 tidy, you need to unite
year into a single column.
Tidyverse Will Not Download Into R Studio Mac Os
unite(), give the
col argument a character string to use as the name of the new column to create. Then list the columns to combine. Finally, give the
sep argument a separator character to use to paste together the values in the cells of each column.
Tidyverse Will Not Download Into R Studio Mac Download
- Create a copy of the original data set
- Paste together the values of the listed columns in a vectorized (i.e. rowwise) fashion.
unite()will place the value of
sepbetween each value during the paste process.
- Append the results as a new column whose name is the value of
Remove the listed columns. To retain the columns in the result, add the argument
remove = FALSE.Since this is easier to see than explain, you may want to study the diagram and result above.
_as a separator character. To avoid a separator character, use
sep = '.